Collaborative filtering is a type of recommendation system that analyzes user interactions and behavior to make personalized recommendations for content, products, or services. It uses machine learning algorithms to identify patterns and relationships in user data, helping to automate the process of finding relevant recommendations. Collaborative filtering can improve user experience, increase engagement, and enhance customer retention and loyalty. It has been successfully applied in various industries, including e-commerce, entertainment, and social networking. To ensure the accuracy and effectiveness of collaborative filtering systems, performance metrics such as precision, recall, and F1 score are used to evaluate their performance. As recommendation systems continue to evolve, collaborative filtering is expected to play a vital role in the future of personalized recommendations.
You couldn’t have missed it: ChatGPT, the artificial intelligence developed by OpenAI, is making a lot of noise. And for good reason, some already think that AI could replace web editors… But for now, the expertise of an SEO web editor remains irreplaceable! Let’s see together how to optimize your ChatGPT prompts, the requests you […]
Kafka is an extremely important 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺 to understand as it was the first of its kind and most of the new products are built on the ideas of Kafka. 𝗦𝗼𝗺𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝗹 𝗱𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻𝘀: ➡️ Clients writing to Kafka are called 𝗣𝗿𝗼𝗱𝘂𝗰𝗲𝗿𝘀, ➡️ Clients reading the Data are called 𝗖𝗼𝗻𝘀𝘂𝗺𝗲𝗿𝘀. ➡️ Data is written into […]
Database isolation allows a transaction to execute as if there are no other concurrently running transactions. The diagram below illustrates four isolation levels. 🔹Serializalble: This is the highest isolation level. Concurrent transactions are guaranteed to be executed in sequence. 🔹Repeatable Read: Data read during the transaction stays the same as the transaction starts. 🔹Read Committed: […]
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready […]
“A crucial contribution to development of a new technology that will impact all of our lives.” –Laura Shin, host of the Unchained podcast and author of The Cryptopians: Idealism, Greed, Lies, and the Making of the First Big Cryptocurrency Craze
“Vitalik Buterin is one of the most influential creators of our generation….Like most of his work, it is sure to become a must-read.”–Camila Russo, author of The Infinite Machine, founder of The Defiant
The ideas behind Ethereum in the words of its founder, describing a radical vision for more than a digital currency—reinventing organizations, economics, and democracy itself in the age of the internet.
Use business intelligence to power corporate growth, increase efficiency, and improve corporate decision making. With this practical book featuring hands-on examples in Power BI with basic Python and R code, you’ll explore the most relevant AI use cases for BI, including improved forecasting, automated classification, and AI-powered recommendations. And you’ll learn how to draw insights from unstructured data sources like text, document, images files.
Author Tobias Zwingmann helps BI professionals, business analysts, and data analytics understand high-impact areas of artificial intelligence. You’ll learn how to leverage popular AI-as-a-service and AutoML platforms to ship enterprise-grade proofs of concept without the help of software engineers or data scientists.
This book shows how blockchain technology can transform the Internet, connecting global businesses in disruptive ways. It offers a comprehensive and multi-faceted examination of the potential of distributed ledger technology (DLT) from a new perspective: as an enabler of the Internet of Value (IoV).
The authors discuss applications of blockchain technology to the financial services domain, e.g. in real estate, insurance and the emerging Decentralised Finance (DeFi) movement. They also cover applications to the media and e-commerce domains. DLT’s impacts on the circular economy, marketplace, Internet of Things (IoT) and oracle business models are also investigated. In closing, the book provides outlooks on the evolution of DLT, as well as the systemic governance and privacy risks of the IoV.
This book introduces to blockchain and deep learning and explores and illustrates the current and new trends that integrate them. The pace and speeds for connectivity are certain on the ascend.
Blockchain and deep learning are twin technologies that are integral to integrity and relevance of network contents. Since they are data-driven technologies, rapidly growing interests exist to incorporate them in efficient and secure data sharing and analysis applications. Blockchain and deep learning are sentinel contemporary research technologies.